PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction (2007.03858v2)

Abstract: Modeling 3D humans accurately and robustly from a single image is very challenging, and the key for such an ill-posed problem is the 3D representation of the human models. To overcome the limitations of regular 3D representations, we propose Parametric Model-Conditioned Implicit Representation (PaMIR), which combines the parametric body model with the free-form deep implicit function. In our PaMIR-based reconstruction framework, a novel deep neural network is proposed to regularize the free-form deep implicit function using the semantic features of the parametric model, which improves the generalization ability under the scenarios of challenging poses and various clothing topologies. Moreover, a novel depth-ambiguity-aware training loss is further integrated to resolve depth ambiguities and enable successful surface detail reconstruction with imperfect body reference. Finally, we propose a body reference optimization method to improve the parametric model estimation accuracy and to enhance the consistency between the parametric model and the implicit function. With the PaMIR representation, our framework can be easily extended to multi-image input scenarios without the need of multi-camera calibration and pose synchronization. Experimental results demonstrate that our method achieves state-of-the-art performance for image-based 3D human reconstruction in the cases of challenging poses and clothing types.

• The paper introduces PaMIR, which fuses parametric SMPL models with deep implicit functions for robust 3D human reconstruction from a single image.
• It employs a novel depth-ambiguity-aware training loss to enhance geometrical consistency between predicted models and high-resolution scans.
• The approach refines body reference optimization to achieve state-of-the-art accuracy across complex poses and varying clothing topologies.

Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction

The presented paper introduces the Parametric Model-Conditioned Implicit Representation (PaMIR), designed to advance the field of 3D human reconstruction from single image inputs. The complexity in reconstructing 3D human models from a single RGB image is primarily attributed to the challenges posed by variable poses, clothing topologies, and the inherent lack of depth information. Traditional methods in the domain often fall short in addressing these challenges due to limitations in their chosen 3D representation forms.

Core Contributions

The paper identifies and tackles the challenge of creating detailed and accurate 3D reconstructions using a novel PaMIR approach by combining parametric body models with deep implicit functions. The key contributions of this approach include:

1. Enhanced Implicit Representation: PaMIR utilizes a combination of SMPL, a parametric body model, to serve as a geometric prior, fused with a non-parametric deep implicit surface representation. This fusion allows for accurate pose estimation and subsequent reconstruction in scenarios involving complex poses and diverse clothing styles.
2. Depth-Ambiguity-Aware Training Loss: To address the issue of depth ambiguity, the authors propose a novel training loss that adapts to discrepancies in predicted and ground-truth models. This loss function helps to bridge the gap in geometrical consistency between the predicted SMPL models and the ground-truth high-resolution scans.
3. Body Reference Optimization: This method further refines the parametric model by improving alignment between predicted SMPL models and real-world imagery data at the inference stage, leading to better generalization and accuracy across varied datasets.

The authors demonstrate that the PaMIR-based framework can be readily adapted to process multi-image inputs, enhancing its applicability without requiring extensive multi-camera setups or exact pose synchronization.

Numerical and Experimental Results

Quantitative evaluations reveal that PaMIR outperforms existing state-of-the-art methods like PIFu and other traditional parametric and non-parametric techniques in terms of point-to-surface and Chamfer distance metrics. Furthermore, qualitative comparisons illustrate PaMIR’s superior robustness to self-occlusions and challenging body poses that typically confound other methodologies.

Besides showcasing impressive numerical results, PaMIR’s multi-modality is highlighted—indicating its capacity to generate viable alternative reconstructions given different plausible SMPL-based pose interpretations. This property is particularly important for scenarios involving incomplete or ambiguous initial data.

Implications and Future Directions

Practically, PaMIR’s strength lies in its ability to produce highly-detailed 3D models suitable for applications in virtual environments, AR/VR content creation, and telepresence. Theoretically, this work advances the domain's understanding of integrating semantic priors with neural implicit shape representations, thus opening new avenues for research in the robustness of machine learning-based 3D reconstructions.

Looking forward, future work could focus on reducing dependency on extensive curated training data, possibly by leveraging large-scale unsupervised datasets or improving models' adaptability to real-world dynamics through enhanced temporal consistency in video inputs. Consequently, this could significantly broaden PaMIR’s applicability across unstructured environments and dynamic activities.

In sum, the introduction of PaMIR represents a noteworthy progression in 3D human modeling, merging the merits of parametric and non-parametric approaches to address longstanding challenges in computer vision and graphics.